
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					bool = True     ,				UpperCamelCase__     :					bool = False       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			scheduler
    __magic_name__								=			optimizers if isinstance(UpperCamelCase__     ,				(list, tuple)       ) else [optimizers]
    __magic_name__								=			split_batches
    __magic_name__								=			step_with_optimizer
    __magic_name__								=			GradientState()
  def 				_lowercase				(					self     :					int     ,				*UpperCamelCase__     :					List[str]     ,				**UpperCamelCase__     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    if not self.step_with_optimizer:
      # No link between scheduler and optimizer -> just step
      self.scheduler.step(*UpperCamelCase__     ,				**UpperCamelCase__       )
      return

    # Otherwise, first make sure the optimizer was stepped.
    if not self.gradient_state.sync_gradients:
      if self.gradient_state.adjust_scheduler:
        self.scheduler._step_count += 1
      return

    for opt in self.optimizers:
      if opt.step_was_skipped:
        return
    if self.split_batches:
      # Split batches -> the training dataloader batch size is not changed so one step per training step
      self.scheduler.step(*UpperCamelCase__     ,				**UpperCamelCase__       )
    else:
      # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
      # num_processes steps per training step
      __magic_name__								=			AcceleratorState().num_processes
      for _ in range(UpperCamelCase__       ):
        # Special case when using OneCycle and `drop_last` was not used
        if hasattr(self.scheduler     ,				"""total_steps"""       ):
          if self.scheduler._step_count <= self.scheduler.total_steps:
            self.scheduler.step(*UpperCamelCase__     ,				**UpperCamelCase__       )
        else:
          self.scheduler.step(*UpperCamelCase__     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      Dict:







    """simple docstring"""






    return self.scheduler.get_last_lr()
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Dict:







    """simple docstring"""






    return self.scheduler.state_dict()
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					str       )     ->      Dict:







    """simple docstring"""






    self.scheduler.load_state_dict(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any       )     ->      Optional[Any]:







    """simple docstring"""






    return self.scheduler.get_lr()





  def 				_lowercase				(					self     :					Dict     ,				*UpperCamelCase__     :					Dict     ,				**UpperCamelCase__     :					int       )     ->      Optional[Any]:







    """simple docstring"""






    return self.scheduler.print_lr(*UpperCamelCase__     ,				**UpperCamelCase__       )



from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase     :	Union[str, Any]       			=  {
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase     :	Optional[Any]       			=  [
        'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'InformerForPrediction',
        'InformerModel',
        'InformerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_informer import (
            INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            InformerForPrediction,
            InformerModel,
            InformerPreTrainedModel,
        )

else:
    import sys

    __lowerCAmelCase     :	List[str]       			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

